Recovery Process Optimization Using Survival Regression
FFA Working Paper 4/2020, University of Economics, Prague.
25 Pages Posted: 30 Sep 2020
Date Written: August 14, 2020
The goal of this paper is to propose, empirically test and compare different logistic and survival analysis techniques in order to optimize the debt collection process. This process uses various actions, such as phone calls, mails, visits, or legal steps to recover past due loans. We focus on the soft collection part, where the question is whether and when to call a past-due debtor with regard to the expected financial return of such an action. We propose using the survival analysis technique, in which the phone call can be compared to a medical treatment, and repayment to the recovery of a patient. We show on a real banking dataset that, unlike ordinary logistic regression, this model provides the expected results and can be efficiently used to optimize the soft collection process.
Keywords: credit risk modelling, survival analysis, scoring, receivables, debt recovery, collection, retail banking, credit risk
JEL Classification: G21, G28, C14
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